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Background: The serum proteomic test VeriStrat has been shown to be able to classify advanced non-small cell lung cancer (NSCLC) patients for overall survival (OS) after treatment with epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs). In this study, VeriStrat was evaluated as a pre-treatment stratification tool in patients with advanced stage NSCLC for treatment with the combination of erlotinib and sorafenib, considering both OS and progression-free survival (PFS) as end points.Methods: Serum samples from 50 patients treated within the context of a phase II trial of first-line erlotinib and sorafenib were analysed with VeriStrat, a fully locked mass spectrometry-based test that identifies patients likely to have good or poor outcome on EGFR therapy based on eight distinct features in mass spectra. Analysis was performed fully blinded to all clinical data, and then the outcome data were analysed with respect to the obtained serum classifications.Results: VeriStrat classified pre-treatment samples into two groups, VeriStrat Good and VeriStrat Poor, which were significantly different in OS (hazard ratio (HR) 0.30, log-rank P=0.009) and in PFS (HR 0.40, log-rank P=0.035).Conclusion: VeriStrat has shown its potential for stratification of unselected, advanced stage NSCLC patients treated in first line with a combination of erlotinib and sorafenib. [ABSTRACT FROM AUTHOR]

Background: VeriStrat is a blood-based proteomic test with predictive and prognostic significance in second-line treatments for non-small cell lung cancer (NSCLC). This trial was designed to investigate the role of VeriStrat in first-line treatment of advanced NSCLC with standard chemotherapy. Here we present the results for 76 non-squamous patients treated with a combination of carboplatin or cisplatin with pemetrexed.Methods: The test-assigned classifications of VeriStrat Good or VeriStrat Poor to samples collected at baseline. The primary end point was progression-free survival (PFS); secondary end points included overall survival (OS) and objective response. Exploratory analyses of end points separately in carboplatin/pemetrexed and cisplatin/pemetrexed subgroups were also conducted.Results: Patients classified as VeriStrat Good had longer PFS and OS than VeriStrat Poor: 6.5 vs 1.6 months and 10.8 vs 3.4 months, respectively; the corresponding hazard ratios (HRs) were 0.36 (P<0.0001) and 0.26 (P<0.0001); they were also more likely to achieve objective response. Prognostic significance of VeriStrat was confirmed in multivariate analysis. Significant differences in OS and PFS between Veristrat classifications were also found when treatment subgroups were analysed separately.Conclusions: The trial demonstrated clinical utility of VeriStrat as a prognostic test for standard first-line chemotherapy of non-squamous advanced NSCLC. [ABSTRACT FROM AUTHOR]

Background:The serum proteomic test VeriStrat has been shown to be able to classify advanced non-small cell lung cancer (NSCLC) patients for overall survival (OS) after treatment with epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs). In this study, VeriStrat was evaluated as a pre-treatment stratification tool in patients with advanced stage NSCLC for treatment with the combination of erlotinib and sorafenib, considering both OS and progression-free survival (PFS) as end points.Methods:Serum samples from 50 patients treated within the context of a phase II trial of first-line erlotinib and sorafenib were analysed with VeriStrat, a fully locked mass spectrometry-based test that identifies patients likely to have good or poor outcome on EGFR therapy based on eight distinct features in mass spectra. Analysis was performed fully blinded to all clinical data, and then the outcome data were analysed with respect to the obtained serum classifications.Results:VeriStrat classified pre-treatment samples into two groups, VeriStrat Good and VeriStrat Poor, which were significantly different in OS (hazard ratio (HR) 0.30, log-rank P=0.009) and in PFS (HR 0.40, log-rank P=0.035).Conclusion:VeriStrat has shown its potential for stratification of unselected, advanced stage NSCLC patients treated in first line with a combination of erlotinib and sorafenib. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma (HCC) is one of the fastest growing causes of cancer-related death. Guidelines recommend obtaining a screening ultrasound with or without alpha-fetoprotein (AFP) every 6 months in at-risk adults. AFP as a screening biomarker is plagued by low sensitivity/specificity, prompting interest in discovering alternatives. Mass spectrometry-based techniques are promising in their ability to identify potential biomarkers. This study aimed to use machine learning utilizing spectral data and AFP to create a model for early detection. Serum samples were collected from three separate cohorts, and data were compiled to make Development, Internal Validation, and Independent Validation sets. AFP levels were measured, and Deep MALDI ® analysis was used to generate mass spectra. Spectral data were input into the VeriStrat ® classification algorithm. Machine learning techniques then classified each sample as "Cancer" or "No Cancer". Sensitivity and specificity of the test were >80% to detect HCC. High specificity of the test was independent of cause and severity of underlying disease. When compared to AFP, there was improved cancer detection for all tumor sizes, especially small lesions. Overall, a machine learning algorithm incorporating mass spectral data and AFP values from serum samples offers a novel approach to diagnose HCC. Given the small sample size of the Independent Validation set, a further independent, prospective study is warranted.

Introduction: Most diseases involve a complex interplay between multiple biological processes at the cellular, tissue, organ, and systemic levels. Clinical tests and biomarkers based on the measurement of a single or few analytes may not be able to capture the complexity of a patient's disease. Novel approaches for comprehensively assessing biological processes from easily obtained samples could help in the monitoring, treatment, and understanding of many conditions., Objectives: We propose a method of creating scores associated with specific biological processes from mass spectral analysis of serum samples., Methods: A score for a process of interest is created by: (i) identifying mass spectral features associated with the process using set enrichment analysis methods, and (ii) combining these features into a score using a principal component analysis-based approach. We investigate the creation of scores using cohorts of patients with non-small cell lung cancer, melanoma, and ovarian cancer. Since the circulating proteome is amenable to the study of immune responses, which play a critical role in cancer development and progression, we focus on functions related to the host response to disease., Results: We demonstrate the feasibility of generating scores, their reproducibility, and their associations with clinical outcomes. Once the scores are constructed, only 3 µL of serum is required for the assessment of multiple biological functions from the circulating proteome., Conclusion: These mass spectrometry-based scores could be useful for future multivariate biomarker or test development studies for informing treatment, disease monitoring and improving understanding of the roles of various biological functions in multiple disease settings., Competing Interests: Joanna Roder, Heinrich Roder, Julia Grigorieva, Lelia Net, Senait Asmellash, Carlos Oliveira, and Krista Meyer are or were employees of Biodesix, Inc. and have or had stock or stock options in Biodesix, Inc. Joanna Roder, Heinrich Roder and Carlos Oliveria are inventors on related patents assigned to Biodesix, Inc. Sabine Kasimir-Bauer, Harvey Pass and Jeffrey Weber declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2020 The Authors.)

Objective: Timely assessment of patient-specific prognosis is critical to oncology care involving a shared decision-making approach, but clinical prognostic factors traditionally used in NSCLC have limitations. We examine a proteomic test to address these limitations., Methods: This study examines the prognostic performance of the VeriStrat blood-based proteomic test that measures the inflammatory disease state of patients with advanced NSCLC. A systematic literature review (SLR) was performed, yielding cohorts in which the hazard ratio (HR) was reported for overall survival (OS) of patients with VeriStrat Poor (VSPoor) test results versus VeriStrat Good (VSGood). A study-level meta-analysis of OS HRs was performed in subgroups defined by lines of therapy and treatment regimens., Results: Twenty-four cohorts met SLR criteria. Meta-analyses in five subgroups (first-line platinum-based chemotherapy, second-line single-agent chemotherapy, first-line EGFR-tyrosine kinase inhibitor (TKI) therapy, and second- and higher-line TKI therapy, and best supportive care) resulted in statistically significant ( p  ≤ .001) summary effect sizes for OS HRs of 0.42, 0.54, 0.41, 0.52, and 0.50, respectively, indicating increased OS by about two-fold for patients who test VSGood. No significant heterogeneity was seen in any subgroup ( p  > .05)., Conclusions: Advanced NSCLC patients classified VSGood have significantly longer OS than those classified VSPoor. The summary effect size for OS HRs around 0.4-0.5 indicates that the expected median survival of those with a VSGood classification is approximately 2-2.5 times as long as those with VSPoor. The robust prognostic performance of the VeriStrat test across various lines of therapy and treatment regimens has clinical implications for treatment shared decision-making and potential for novel treatment strategies.

The remarkable success of immune checkpoint inhibitors (ICIs) has given hope of cure for some patients with advanced cancer; however, the fraction of responding patients is 15-35%, depending on tumor type, and the proportion of durable responses is even smaller. Identification of biomarkers with strong predictive potential remains a priority. Until now most of the efforts were focused on biomarkers associated with the assumed mechanism of action of ICIs, such as levels of expression of programmed death-ligand 1 (PD-L1) and mutation load in tumor tissue, as a proxy of immunogenicity; however, their performance is unsatisfactory. Several assays designed to capture the complexity of the disease by measuring the immune response in tumor microenvironment show promise but still need validation in independent studies. The circulating proteome contains an additional layer of information characterizing tumor-host interactions that can be integrated into multivariate tests using modern machine learning techniques. Here we describe several validated serum-based proteomic tests and their utility in the context of ICIs. We discuss test performances, demonstrate their independence from currently used biomarkers, and discuss various aspects of associated biological mechanisms. We propose that serum-based multivariate proteomic tests add a missing piece to the puzzle of predicting benefit from ICIs.

The therapeutic landscape in metastatic melanoma has changed dramatically in the last decade, with the success of immune checkpoint inhibitors resulting in durable responses for a large number of patients. For patients with BRAF mutations, combinations of BRAF and MEK inhibitors demonstrated response rates and benefit comparable to those from immune checkpoint inhibitors, providing the rationale for sequential treatment with targeted and immunotherapies and raising the question of optimal treatment sequencing.Biomarkers for the selection of anti-PD-1 therapy in BRAF wild type (BRAF WT) and in BRAF mutated (BRAF MUT) patients help development of alternative treatments for patients unlikely to benefit, and might lead to better understanding of the interaction of checkpoint inhibition and targeted therapy. In this paper we evaluate the performance of a previously developed serum proteomic test, BDX008, in metastatic melanoma patients treated with anti-PD-1 agents and investigate the role of BRAF mutation status. BDX008, a pre-treatment proteomic test associated with acute phase reactants, wound healing and complement activation, stratifies patients into two groups, BDX008+ and BDX008-, with better and worse outcomes on immunotherapy.Serum samples were available from 71 patients treated with anti-PD1 inhibitors; 25 patients had BRAF mutations, 39 were wild type. Overall, BDX008+ patients had significantly better overall survival (OS) (HR = 0.50, P = 0.016) and a trend for better progression-free survival (PFS) (HR = 0.61, P = 0.060) than BDX008- patients. BDX008 classification was statistically significant in the analyses adjusted for mutation status, LDH, and line of treatment (P = 0.009 for OS and 0.031 for PFS). BRAF WT BDX008+ patients had markedly long median OS of 32.5 months and 53% landmark 2 years survival, with statistically significantly superior OS as compared to BDX008- patients (HR = 0.41, P = 0.032). The difference between BDX008+ and BDX008- in PFS in BRAF WT patients and in OS and PFS in BRAF MUT patients did not reach statistical significance, though numerically was consistent with overall results. The test demonstrated significant interaction with neutrophil-to-lymphocyte ratio (NLR) (PFS P = 0.041, OS P = 0.004). BDX008 as a biomarker selecting for benefit from immune checkpoint blockade, especially in patients with wild type BRAF and in subgroups with low NLR, warrants further evaluation.

Objectives: VeriStrat ® is a blood-based test that utilizes matrix-assisted laser desorption/ionization time-of-flight (MALDI ToF) mass spectrometry to assign a binary classification of VeriStrat Good or VeriStrat Poor that is associated with treatment outcomes in cancer patients. A number of other studies have shown an association between VeriStrat status and clinical outcomes in second and subsequent lines of therapy. The prognostic properties of VeriStrat were demonstrated in the placebo arms of two randomized studies in non-small cell lung cancer (NSCLC): TOPICAL and BR.21; the predictive properties of the test were shown in a prospective randomized phase III study PROSE in the second line treatment of NSCLC with erlotinib versus chemotherapy. Motivated by these observations, we sought to extend the clinical utility of VeriStrat to standard first line chemotherapy and evaluated the performance of the test in a number of clinical studies of patients treated with platinum-based regimens., Materials and Methods: We examine the performance of VeriStrat in three independent clinical trials where the test classification was acquired for prospectively collected baseline samples from 481 patients treated with platinum-based chemotherapy in first line., Results: Across these trials, 66-70% of patients were classified as VeriStrat Good; patients classified as VeriStrat Good had significantly longer progression-free survival and overall survival than VeriStrat Poor patients, with hazard ratios ranging from 0.36 to 0.72 and 0.26 to 0.51, respectively. These results demonstrated that VeriStrat is a strong prognostic test in NSCLC patients treated with platinum-based regimens in the first line., Conclusion: VeriStrat provides valuable clinical information that may be used to support patient-physician conversations regarding prognosis and treatment options, and to identify a subset of patients who might benefit from other treatment strategies, possibly in the framework of clinical trials., (Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.)

A mass spectrometry analysis was performed using serum from patients receiving checkpoint inhibitors to define baseline protein signatures associated with outcome in metastatic melanoma. Pretreatment serum was obtained from a development set of 119 melanoma patients on a trial of nivolumab with or without a multipeptide vaccine and from patients receiving pembrolizumab, nivolumab, ipilimumab, or both nivolumab and ipilimumab. Spectra were obtained using matrix-assisted laser desorption/ionization time of flight mass spectrometry. These data combined with clinical data identified patients with better or worse outcomes. The test was applied to five independent patient cohorts treated with checkpoint inhibitors and its biology investigated using enrichment analyses. A signature consisting of 209 proteins or peptides was associated with progression-free and overall survival in a multivariate analysis. The test performance across validation cohorts was consistent with the development set results. A pooled analysis, stratified by set, demonstrated a significantly better overall survival for "sensitive" relative to "resistant" patients, HR = 0.15 (95% confidence interval: 0.06-0.40, P < 0.001). The test was also associated with survival in a cohort of ipilimumab-treated patients. Test classification was found to be associated with acute phase reactant, complement, and wound healing pathways. We conclude that a pretreatment signature of proteins, defined by mass spectrometry analysis and machine learning, predicted survival in patients receiving PD-1 blocking antibodies. This signature of proteins was associated with acute phase reactants and elements of wound healing and the complement cascade. This signature merits further study to determine if it identifies patients who would benefit from PD-1 blockade. Cancer Immunol Res; 6(1); 79-86. ©2017 AACR ., (©2017 American Association for Cancer Research.)

This trial was designed to assess efficacy and safety of erlotinib with sorafenib in the treatment of patients with advanced pancreatic adenocarcinoma. An exploratory correlative study analyzing pretreatment serum samples using a multivariate protein mass spectrometry-based test (VeriStrat®), previously shown to correlate with outcomes in lung cancer patients treated with erlotinib, was performed. Patients received sorafenib 400 mg daily along with erlotinib 150 mg daily with a primary endpoint of 8-week progression free survival (PFS) rate. Pretreatment serum sample analysis by VeriStrat was done blinded to clinical and outcome data; the endpoints were PFS and overall survival (OS). Difference between groups (by VeriStrat classification) was assessed using log-rank P values; hazard ratios (HR) were obtained from Cox proportional hazards model. Thirty-six patients received study drug and were included in the survival analysis. Eight-week PFS rate of 46% (95% confidence interval (CI): 0.32-0.67) did not meet the primary endpoint of a rate ≥70%. Thirty-two patients were included in the correlative analysis, and VeriStrat "Good" patients had superior PFS (HR = 0.18, 95% CI: 0.06-0.57; P = 0.001) and OS (HR = 0.31 95% CI: 0.13-0.77, P = 0.008) compared to VeriStrat "Poor" patients. Grade 3 toxicities of this regimen included fever, anemia, diarrhea, dehydration, rash, and altered liver function. This study did not meet the primary endpoint, and this combination will not be further pursued. In this small retrospective analysis, the proteomic classification was significantly associated with clinical outcomes and is being further evaluated in ongoing studies., (© 2014 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.)

Background: An established multivariate serum protein test can be used to classify patients according to whether they are likely to have a good or poor outcome after treatment with EGFR tyrosine-kinase inhibitors. We assessed the predictive power of this test in the comparison of erlotinib and chemotherapy in patients with non-small-cell lung cancer., Methods: From Feb 26, 2008, to April 11, 2012, patients (aged ≥18 years) with histologically or cytologically confirmed, second-line, stage IIIB or IV non-small-cell lung cancer were enrolled in 14 centres in Italy. Patients were stratified according to a minimisation algorithm by Eastern Cooperative Oncology Group performance status, smoking history, centre, and masked pretreatment serum protein test classification, and randomly assigned centrally in a 1:1 ratio to receive erlotinib (150 mg/day, orally) or chemotherapy (pemetrexed 500 mg/m(2), intravenously, every 21 days, or docetaxel 75 mg/m(2), intravenously, every 21 days). The proteomic test classification was masked for patients and investigators who gave treatments, and treatment allocation was masked for investigators who generated the proteomic classification. The primary endpoint was overall survival and the primary hypothesis was the existence of a significant interaction between the serum protein test classification and treatment. Analyses were done on the per-protocol population. This trial is registered with ClinicalTrials.gov, number NCT00989690., Findings: 142 patients were randomly assigned to chemotherapy and 143 to erlotinib, and 129 (91%) and 134 (94%), respectively, were included in the per-protocol analysis. 88 (68%) patients in the chemotherapy group and 96 (72%) in the erlotinib group had a proteomic test classification of good. Median overall survival was 9·0 months (95% CI 6·8-10·9) in the chemotherapy group and 7·7 months (5·9-10·4) in the erlotinib group. We noted a significant interaction between treatment and proteomic classification (pinteraction=0·017 when adjusted for stratification factors; pinteraction=0·031 when unadjusted for stratification factors). Patients with a proteomic test classification of poor had worse survival on erlotinib than on chemotherapy (hazard ratio 1·72 [95% CI 1·08-2·74], p=0·022). There was no significant difference in overall survival between treatments for patients with a proteomic test classification of good (adjusted HR 1·06 [0·77-1·46], p=0·714). In the group of patients who received chemotherapy, the most common grade 3 or 4 toxic effect was neutropenia (19 [15%] vs one [<1%] in the erlotinib group), whereas skin toxicity (one [<1%] vs 22 [16%]) was the most frequent in the erlotinib group., Interpretation: Our findings indicate that serum protein test status is predictive of differential benefit in overall survival for erlotinib versus chemotherapy in the second-line setting. Patients classified as likely to have a poor outcome have better outcomes on chemotherapy than on erlotinib., Funding: Italian Ministry of Health, Italian Association of Cancer Research, and Biodesix., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Purpose: In a multicenter randomized phase II trial of gemcitabine (arm A), erlotinib (arm B), and gemcitabine and erlotinib (arm C), similar progression-free survival (PFS) and overall survival (OS) were observed in all arms. We performed an exploratory, blinded, retrospective analysis of plasma or serum samples collected as part of the trial to investigate the ability of VeriStrat (VS) to predict treatment outcomes., Methods: Ninety-eight patients were assessable, and the majority had stage IV disease (81%), adenocarcinoma histology (63%), reported current or previous tobacco use (84%), and 26% had a performance status (PS) of 2., Results: In arm A, patients with VS Good (n = 20) compared with VS Poor status (n = 8) had similar PFS (hazard ratio [HR]: 1.21; p = 0.67) and OS (HR: 0.82; p = 0.64). In arm B, patients with VS Good (n = 26) compared with VS Poor (n = 12) had a statistically significantly superior PFS (HR: 0.33; p = 0.002) and OS (HR: 0.40; p = 0.014). In arm C, patients with VS Good (n = 17) compared with Poor (n = 1 5) had a superior PFS (HR: 0.42; p = 0.027) and a trend toward superior OS (HR: 0.48; p = 0.051). In the multivariate analysis for PFS, VS status was statistically significant (p = 0.011); for OS, VS status (p = 0.017) and PS (p = 0.005) were statistically significant. A statistically significant VS and treatment interaction (gemcitabine versus erlotinib) was observed for PFS and OS., Conclusions: Gemcitabine is the superior treatment for elderly patients with VS Poor status. First-line erlotinib for elderly patients with VS Good status may warrant further investigation.

Background: VeriStrat(®) is a serum proteomic test used to determine whether patients with advanced non-small cell lung cancer (NSCLC) who have already received chemotherapy are likely to have good or poor outcomes from treatment with gefitinib or erlotinib. The main objective of our retrospective study was to evaluate the role of VS as a marker of overall survival (OS) in patients treated with erlotinib and bevacizumab in the first line., Patients and Methods: Patients were pooled from two phase II trials (SAKK19/05 and NTR528). For survival analyses, a log-rank test was used to determine if there was a statistically significant difference between groups. The hazard ratio (HR) of any separation was assessed using Cox proportional hazards models., Results: 117 patients were analyzed. VeriStrat classified patients into two groups which had a statistically significant difference in duration of OS (p = 0.0027, HR = 0.480, 95% confidence interval: 0.294-0.784)., Conclusion: VeriStrat has a prognostic role in patients with advanced, nonsquamous NSCLC treated with erlotinib and bevacizumab in the first line. Further work is needed to study the predictive role of VeriStrat for erlotinib and bevacizumab in chemotherapy-untreated patients., (Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.)

Primary biliary cirrhosis (PBC), primary sclerosing cholangitis (PSC) and autoimmune hepatitis (AIH) are the major forms of autoimmune liver diseases each characterized by the destruction of a specific liver cell type and the presence of differing auto-antibodies. We took a proteomic approach utilizing in situ matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS) to obtain profiles directly from liver samples of patients with PBC, PSC, AIH and controls. The ability to precisely localize the region for acquisition of MALDI MS allowed us to obtain profiles from bile ducts, inflammatory infiltrates and hepatocytes from each biopsy sample. Analysis tools developed to identify peaks and compare peaks across diseases and cell types were used to develop models to classify the samples. Using an initial set of testing samples from PBC patients and controls, we identified unique peaks present in bile ducts, inflammatory infiltrates and hepatocytes that could classify samples in a validation cohort with 88-91% accuracy. Interestingly, profiles of PSC and AIH did not differ significantly from PBC. Identification of proteins in these peaks may represent novel autoantigens or effector molecules. These findings illustrate the potential of a proteomic approach to autoimmune diseases with in situ MALDI MS.

Background: We hypothesized that a serum proteomic profile predictive of survival benefit in non-small cell lung cancer patients treated with epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKI) reflects tumor EGFR dependency regardless of site of origin or class of therapeutic agent., Methods: Pretreatment serum or plasma from 230 patients treated with cetuximab, EGFR-TKIs, or chemotherapy for recurrent/metastatic head and neck squamous cell carcinoma (HNSCC) or colorectal cancer (CRC) were analyzed by mass spectrometry. Each sample was classified into "good" or "poor" groups using VeriStrat, and survival analyses of each cohort were done based on this classification. For the CRC cohort, this classification was correlated with the tumor EGFR ligand levels and KRAS mutation status., Results: In the EGFR inhibitor-treated cohorts, the classification predicted survival (HNSCC: gefitinib, P = 0.007 and erlotinib/bevacizumab, P = 0.02; CRC: cetuximab, P = 0.0065) whereas the chemotherapy cohort showed no survival difference. For CRC patients, tumor EGFR ligand RNA levels were significantly associated with the proteomic classification, and combined KRAS and proteomic classification provided improved survival classification., Conclusions: Serum proteomic profiling can detect clinically significant tumor dependence on the EGFR pathway in non-small cell lung cancer, HNSCC, and CRC patients treated with either EGFR-TKIs or cetuximab. This classification is correlated with tumor EGFR ligand levels and provides a clinically practical way to identify patients with diverse cancer types most likely to benefit from EGFR inhibitors. Prospective studies are necessary to confirm these findings.

We assessed proteomic patterns in breast cancer using MALDI MS and laser capture microdissected cells. Protein and peptide expression in invasive mammary carcinoma versus normal mammary epithelium and estrogen-receptor positive versus estrogen-receptor negative tumors were compared. Biomarker candidates were identified by statistical analysis and classifiers were developed and validated in blinded test sets. Several of the m/ z features used in the classifiers were identified by LC-MS/MS and two were confirmed by immunohistochemistry.

Background: Some but not all patients with non-small-cell lung cancer (NSCLC) respond to treatment with epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs). We developed and tested the ability of a predictive algorithm based on matrix-assisted laser desorption ionization (MALDI) mass spectrometry (MS) analysis of pretreatment serum to identify patients who are likely to benefit from treatment with EGFR TKIs., Methods: Serum collected from NSCLC patients before treatment with gefitinib or erlotinib were analyzed by MALDI MS. Spectra were acquired independently at two institutions. An algorithm to predict outcomes after treatment with EGFR TKIs was developed from a training set of 139 patients from three cohorts. The algorithm was then tested in two independent validation cohorts of 67 and 96 patients who were treated with gefitinib and erlotinib, respectively, and in three control cohorts of patients who were not treated with EGFR TKIs. The clinical outcomes of survival and time to progression were analyzed., Results: An algorithm based on eight distinct m/z features was developed based on outcomes after EGFR TKI therapy in training set patients. Classifications based on spectra acquired at the two institutions had a concordance of 97.1%. For both validation cohorts, the classifier identified patients who showed improved outcomes after EGFR TKI treatment. In one cohort, median survival of patients in the predicted "good" and "poor" groups was 207 and 92 days, respectively (hazard ratio [HR] of death in the good versus poor groups = 0.50, 95% confidence interval [CI] = 0.24 to 0.78). In the other cohort, median survivals were 306 versus 107 days (HR = 0.41, 95% CI = 0.17 to 0.63). The classifier did not predict outcomes in patients who did not receive EGFR TKI treatment., Conclusion: This MALDI MS algorithm was not merely prognostic but could classify NSCLC patients for good or poor outcomes after treatment with EGFR TKIs. This algorithm may thus assist in the pretreatment selection of appropriate subgroups of NSCLC patients for treatment with EGFR TKIs.

Monoclonal antibodies of two clones reacting with the nonnative forms of d-glyceraldehyde-3-phosphate dehydrogenase, EC 1.2.1.12 (GAPDH), were obtained. Antibodies of clone 6C5 belonged to IgG1 subtype; antibodies of clone 6G7 belonged to IgM type. The interaction of antibodies of both clones with the immobilized and soluble enzyme was studied. The specificity of antibodies to the definite oligomeric forms was demonstrated on immobilized monomers, dimers, and tetramers of GAPDH. The affinity of antibodies to monomeric and dimeric forms of GAPDH, either active or not, was demonstrated. At the same time the antibodies did not react with the tetrameric enzyme. The binding of antibodies had no influence on the enzymatic activity. However, the addition of antibodies to the denatured enzyme blocked the spontaneous renaturation of GAPDH. The immobilized antibodies of both clones were successfully used for the purification of GAPDH solution from the denatured admixtures.